Conformal prediction for reliable machine learning : theory, adaptations and applications - Balasubramanian, Vineeth; Ho, Shen-Shyang; Vovk, Vladimir, 1960- (Morgan Kaufmann, 2014)
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly.